Hey everyone,

BizLocationDB is my attempt at building a high-quality POI dataset for popular chains and brands. I scraped public store locators to build this. Currently, there are not many brands but I am adding more continuously.

There are similar datasets like AllThePlaces and OpenStreetMap, but I’ve found them to be incomplete and messy to work with. For example, AllThePlaces has around 34k Starbucks features, while there are actually 40k+. Location identifiers are also not standardized. Sometimes, city, zip might be missing, the same coordinate could have multiple cities pointing to it, etc.

With BizLocationDB I wanted to focus more on accuracy, rather than just adding features blindly.

There are two tiers, free allows you to have the same data with lesser accuracy and lesser fields, and premium allows you to have the full data. I think that way I can keep it sustainable, and still have reasonable usefulness with the free version.

Feedback is appreciated.

Disclosure: I built this dataset and publish it on my own site. Posting under rule 1.

It covers 183 non-financial Korean listed companies, built entirely from DART (dart.fss.or.kr — Korea’s regulatory filing system). Every figure carries the receipt number of the filing it was read from, so any row traces back to the original document.

Coverage

• 183 non-financial listed companies (financials excluded — different balance-sheet structure)
• FY2021–FY2025 annual figures, plus 2026 H1 where filed
• Base date 2026-08-14; share counts and closing prices from the exchange snapshot that day

38 columns — market capitalisation, equity (total and controlling-interest), net profit (total and controlling-interest), net cash, P/B on both equity bases, P/E, listed share count vs. the count printed in the annual report, treasury shares, cancellation history, dividend per share, sector, and two receipt-number columns.

Access — the sortable table and the per-company pages are free and ungated, and the JSON the table loads is a plain static file. The CSV sits behind an email form; disclosing that rather than pretending otherwise. Nothing is paid.

Source: https://accidentalorder.com/en/data/adjusted-valuation/ — methodology at /en/data/methodology/

The part that may actually be useful here: an XBRL aggregation trap

I nearly shipped 29 wrong rows last week, and the failure mode seems general enough to be worth writing down.

I define total borrowings as the sum of every leaf line on the balance sheet whose account name contains “borrowing” or “bond”, excluding leases. Name matching, not account-code mapping — because mapping the four component accounts (short-term borrowings, current portion of long-term debt, long-term borrowings, bonds) breaks on real filings in at least six distinct ways: single aggregate lines, missing standard codes, mezzanine instruments as separate accounts, parenthetical annotations in the account name, and genuine zeros. Name matching survives all six.

It does not survive a company that splits the balance sheet only into financial and non-financial liabilities. Those companies keep their borrowings inside captions like “current financial liabilities” — no account name contains “borrowing”, none contains “bond”, and the scan returns zero.

Korea Electric Power came out of my pipeline with zero borrowings. It carries about KRW 150tn of them.

Two things I’d tell my past self:

1. A completeness check that can’t fire on a zero isn’t a completeness check. Mine was “total borrowings must not exceed total liabilities” — an upper bound, when the failure mode was at the lower one. Zero passes trivially. Ask what your guard does when the input is empty, not when it’s wrong.

2. Name-based extraction from XBRL fails silently, not loudly. The tagging is a floor, not a ceiling: a filer can satisfy every requirement while presenting liabilities at a level of aggregation that makes your derived figure uncomputable from that filing. You get a plausible number back, not an error.

The fix I landed on wasn’t to compute the missing number but to bound it. Collect every financial-liability line on the balance sheet — derivative captions, “other” captions, everything, making no judgement about what any of them contains — and ask: if all of them were borrowings, how far could the published ratio move?

If it could move net cash / market cap by ≥1 percentage point, don’t publish net cash. Blank cell, flag on the row. If <1pp, publish.

The threshold is denominated in the units of the number I publish, not the units of the thing I’m uncertain about. My first three attempts were all “aggregate financial liabilities as a share of total liabilities > X%” for X in {5, 1, 0} — three arbitrary constants, none of which answers the question that matters, which isn’t “how big is this caption” but “how far can it move what I’m claiming”.

Cost: net cash went from 182 published rows to 97. Positive-net-cash companies from 80 to 51. Companies I’d described as carrying no borrowings at all, from 32 to 15 — 18 of that original 32 were actually sitting on material aggregate financial liabilities, which would have been the most quotable and most wrong line in the writeup.

Benefit: across the 97 that survive, the worst-case bound is 0.951pp, median 0.004pp. So the guarantee needs no footnote — every published figure holds to within one point even if every aggregate caption turned out to be borrowings.

86 of 183 rows now have an empty net-cash cell. That isn’t a gap I’m apologising for; it’s the disclosure regime reported accurately. The alternative to a blank cell is the same blank filled with an assumption and presented as if it were read off a filing.

Happy to go into DART API specifics if anyone’s pulling from it — it’s under-documented and I’ve hit most of its edges by now.

If a company uses AI for digital marketing tasks, such as analytics, content optimization, and fixing technical issues, to augment its marketing funnel, and its competitors do the same, what happens to competitive edge?

Suppose AI agents analyze two competing websites and identify areas for improvement. Website A scores 4 out of10 and has six areas for improvement, while website B scores 6 out of 10 and has four. Both companies use the same AI tools or plugins, such as Claude-based tools, to plug those gaps. Once issues are addressed, both websites could potentially have a similar level of technical and content optimization.

Now imagine 10 to 12 competitors doing the same thing. All of them are leveraging AI to continuously analyze their websites, identify gaps, and implement recommended optimizations. If they all score 10/10 on SEO and GEO, does anyone really have a competitive advantage and how?

In the case of human agents, people interpret data, identify opportunities, form hypotheses, and make strategic decisions differently.

I wanna work on multimodel cancer prediction or prognosis
How can i find datasets for this
I looked on NCI it looks hard for me and image sections are very large sizes
Can you guys suggest me

Not sure if this is the right sub-reddit, but I’m wondering if anyone has knowledge in actual trend within AI companies, especially the bigger labs. No doubt they still have agreement to collect and buy new data for training their models, but is the trend going down?

Similarly for alignement, post training and fine tuning are they actually buying? Or is there a shift towards internalizing the capabilities? I’m seeing big labs hiring for synthetic data generation, sometimes even data annotations… or the other hand I’m also seeing startups getting traction by focusing more the infra for doing RL, alignement etc.. than the data itself (they call it “AI gym” or “world model”).

What are you thoughts on this and where do you think the “traditional” data-selling industry is going?

I pulled all 1.3 million federal contract records from the Government of

Canada’s open data and looked at one question: who’s holding contracts that

are about to come up for renewal?

A few things surprised me.

26,240 federal contracts are currently live, worth $125B. Of the ones that

report a bidder count, 75% drew one bid or none.

And roughly 63 contracts cross into the 12-month planning window every single

week — about $19B of contract value a year.

That last number is the interesting one. Agencies typically start recompete

planning 12–18 months before a contract ends. So the moment a contract crosses

that line is the moment it becomes worth a conversation. It’s all public, but

nobody was watching for it.

So I built something that does. It’s free, it updates itself monthly, and it

shows you which contracts are expiring, who holds them, what they’re worth,

and how contested they were last time.

No signup needed to browse it. Link in the comments.

If you bid on federal work, I’d genuinely like to know whether this is useful

or whether I’ve missed something obvious.

The new list list here: https://www.inc.com/inc5000/2026

Or does anyone have a copy of it? Thanks for any help!!

I have a dataset of all contractor/home service and construction businesses in the US, Puerto Rico and Canada with up to date Google maps info. Would anyone be interested in seeing a sample?

I’ve been building Nomas Research(https://nomas.fyi), a financial data platform designed specifically to make/sanitize financial data easier for AI agents and LLM applications to consume.

The main idea is that traditional financial APIs and websites are generally designed for humans or conventional software. I wanted to make the underlying data much more usable for AI workflows such as RAG, agentic research, training, and automated financial analysis.

What’s available

• AI-agent-ready financial data — structured data that can be directly fetched and consumed by AI agents without having to scrape or parse financial websites.
• SEC/company financial data — structured financial information derived from SEC filings and taxonomies.
• Insider trading data — insider transactions are updated with less than ~1 minute of delay, so agents can work with near-real-time insider activity.
• API access — designed for programmatic access rather than just browsing dashboards.

I’m particularly interested in feedback from people building financial RAG systems, AI agents, financial LLMs, or datasets for model training.

The site is here: https://nomas.fyi

If you’re working on something in this area, I’d be interested to hear what financial data is currently difficult for your agents/models to access or understand.

Everything is hosted on AWS(ECS, RDS and so on)

Let me know what do you guys think. Any suggestion is welcome

I know this question has been asked a bunch in various subreddits, but I wanted to see if folks had tips based on what I’m building.

Basically, I want to build a desktop LCD screen that shows when a flight flies over you within a certain radius and then shows you basic information like the plane, it’s route, etc. I’m an avgeek myself and plane spot at SFO a few hours a week.

When I talked to friends about it, we had lots of ideas like creating alerts for squawk codes, plane types, etc.

Disclaimer: This is a product I intend to sell.

As a bunch of people have already complained, getting this data is actually quite expensive, and I’m really trying to avoid a subscription in the product beyond the hardware.

Right now, the best I have is:

– Use ADSB.lol ‘s free API to get callsigns within a given radius.

– Call FlightRadar24’s paid API with the callsign and cache that data for a month before calling again (DL1 at 1:24PM on Monday’s is usually JFK – LHR for example).

– The above based on historical data at least in the United States seems to have ~95% accuracy w/ around ~$2/mo/user in cost to me. Good not great. Cache hits were ~62%. That’s not assuming some global cache as well, which would yield better numbers as more people use it.

Does any one have other ideas who has explored this a little bit? I’ve been researching for a few days and am in awe by how pricy this industry is.

Made a resource for student researchers aiming to ideate, or improve upon their existing science fair projects.

Hey everyone,

I am working on an indie web game called FeatFighter where players link artists together through their collaborations (like a music version of Six Degrees of Separation).

My goal is to cover music artists globally across all genres. To make the gameplay smooth, I need a massive initial database of tracks with explicitly linked primary and featured artists.

I would love to get your advice, ideas, or recommendations on how to approach this:

1. Are there existing open datasets, dumps, or graph database exports (like MusicBrainz, Wikidata, or Kaggle projects) that already capture artist collaborations well on a global scale?

2. If you had to build a global music collaboration graph today, what strategy would you use for the initial data load to avoid getting blocked by rate limits on streaming APIs?

Any suggestions, links, or technical insights on how to tackle this would be super helpful.

Thanks a lot for your help!

i’m not sure if this is the right thread for this question but where can i find a list of available usernames? i’ve been going back and fourth with support about a username that’s showing available but can’t claim it due to it being “reserved” according to support.

I released a public crypto microstructure dataset for liquidity-stress prediction. I’m looking for feedback on whether the features/labels are useful.

Check it out here: https://huggingface.co/datasets/Goooddy/crypto-lob-stream

I’m looking to build out 30 different use cases for 30 days where I pick up real data from multiple domains for eg – finance, technology, gaming etc. I want to run the data through a business reporting tool I came across and want to convince my peers/seniors in my firm that it’s a great product.

My real question – which all domains should I focus on and where can I real messy data for each of those domains. Any help is appreciated. Thanks.

Hi all,

I was recently working on a machine learning research project and I came across the WILDS paper. The dataset IWildCam seems very interesting for my project and I wanted to experiment with it. Unfortunately I found that currently the download page seems broken. Am I missing something or is that really the case? That would be very unfortunate.

Has anyone recently used that dataset?

I’m building a structured dataset of real-world locations represented in video games.

The current dataset contains close to 3,000 records from more than 380 games.

Two example records look like this:

record_id,game_title,release_year,franchise,primary_genre,location_name,country_iso,latitude,longitude,location_type,setting_status,coordinate_method,verification_level

acw12_0001,Assassin’s Creed,2007.0,assassins_creed,Action Adventure,Jerusalem,IL,31.7683,35.2137,city,real,representative,verified

bra49_0006,2014 FIFA World Cup Brazil,2014.0,fifa_world_cup,Sports,Arena das Dunas,BR,-5.8267,-35.2125,stadium,real,exact,corroborated

The two rows illustrate an important distinction: Arena das Dunas can be represented by an exact point, while Jerusalem is represented by a point that stands for a larger city.

I would appreciate feedback from people who work with public datasets:

1. Should a public preview include source URLs and evidence, or is it reasonable to keep those in a separate research layer?
2. Which additional information would make the records easier to validate or reuse?
3. What kinds of analyses or applications would you personally build with this dataset?

The goal is to improve the data structure and documentation through feedback from people who might actually use it.

Thanks for your feedback 🙂

we are doing research that involves training our model on really big dataset of diffusion technique based synthetic images. but I am unable to trace appropriate free and active Creative common data hubs for it.
please anyone help me work around this

I run freehire, an open-source job aggregator that ingests postings from dozens of ATS platforms (Greenhouse, Lever, Ashby, Workday, etc.), normalizes them into one schema, and runs each through a facet pipeline.

Just exported the whole catalogue to Hugging Face: freehire-jobs — 6,041,471 postings.

Each row is a raw posting (title, company, description, URL, source, posted_at) plus derived facets:

– Dictionary-only (deterministic, no LLM): skills, seniority, category, work_mode, posting_language, employment_type, education_level, english_level, experience_years_min

– Dictionary-first, LLM-filled: countries, regions, cities

– LLM-only: salary_min/max, currency, period — pay is stated in every format imaginable, no dictionary handles that

Company rows carry their own facets too (industries, HQ country, size, YC batch/stage where applicable).

Format: 20 gzip JSONL shards, split by row range for easy streaming/resuming.

Backstory: I originally wanted to train a cheap classifier for seniority/category tagging instead of paying for an LLM call per job. Turned out the dictionary+LLM pipeline already in prod beats a from-scratch classifier by enough that finishing the classifier wasn’t worth it — so I’m sharing the data instead.

Data includes:

lien_number, debtor_name, address, business_owner_name(s), filing_date, status, secured_party, lien_id

I need last 2 yrs of DAX LTP data, 5 second time frame would be perfect but if not even 1 min or 5 min is fine. If you know where I can collect this data from please lmk. Thank you.

Hi everyone, First time posting here. Can i get some datasets in excel to clean and analyze it? I’m just practicing it to enhance my skills. And also where can i get more datasets aside kaggle and creating from chatgpt? Thanks!

Data. Like a professional excel sheet for like ram prices with relation to Al. This is for my college project. It need to have numbers atleast 25 samples. It is for economics.

Hey y’all,

I’m Luke, and I built Stackra for some context. Not selling a ding o dang thing here, just looking to see what this dataset is really capable of.

The corpus is 15 million sites pulled from HTTP, CommonCrawl, CrUX, and Overture.

I have a few thousand elements mapped out, like analytics, tools, vendors, link value, site category, schema, plugins, you name it. It runs back to 2024, and I’ve started putting some of this data user facing.

I find things like being able to identify that the market for funeral homes is dominated by two vendors, that I can identify site directories and their value/trajectory, that custom built websites pass CVW at a higher rate than WP, but rarely crack the top rankings without investor backing.

Weird little stuff like an emulator site that has been highjacked by porn/gambling sites. A popular listing tool that claims their links provide DR, but are explicitly UTM and nofollow. Pinpointing an ICP for a small business from clustering shared linked domains.

I’m curious to see what it can do, what would you want to ask?